Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on facial attributes using Generative Adversarial Networks (GANs). Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. We generate $526$ unique CFIA combinations of facial attributes for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 526000 CFIA samples, thus resulting in an overall 528000 face image samples. {{We present a sequence of experiments to benchmark the attack potential of CFIA samples using four different automatic FRS}}. We introduced a new metric named Generalized Morphing Attack Potential (G-MAP) to benchmark the vulnerability of generated attacks on FRS effectively. Additional experiments are performed on the representative subset of the CFIA dataset to benchmark both perceptual quality and human observer response. Finally, the CFIA detection performance is benchmarked using three different single image based face Morphing Attack Detection (MAD) algorithms. The source code of the proposed method together with CFIA dataset will be made publicly available: \url{https://github.com/jagmohaniiit/LatentCompositionCode}

翻译：人脸操纵攻击因其对人脸识别系统的脆弱性而引起了生物特征研究人员的关注。本文提出了一种基于面部属性的生成对抗网络的新型复合人脸图像攻击方案。给定两个不同数据主体的面部图像，所提出的CFIA方法将独立生成分割的面部属性，然后使用透明掩膜融合以生成CFIA样本。我们为每对贡献数据主体生成了526种独特的面部属性CFIA组合。在我们新生成的CFIA数据集上进行了大量实验，该数据集包含1000个独特身份、2000个真实样本和526000个CFIA样本，共计528000个人脸图像样本。我们通过一系列实验使用四种不同的自动人脸识别系统对CFIA样本的攻击潜力进行了基准测试。我们引入了一种名为广义变形攻击潜力的新指标，以有效评估生成攻击对人脸识别系统的脆弱性。此外，在CFIA数据集代表性子集上进行额外实验，以基准测试感知质量和人类观察者响应。最后，使用三种不同的基于单张图像的变形攻击检测算法对CFIA检测性能进行了基准测试。所提出方法的源代码及CFIA数据集将公开提供：\url{https://github.com/jagmohaniiit/LatentCompositionCode}